Cells rely on complex regulatory networks to sense and respond to environmental cues. The dynamics of the regulatory network governing cellular responses cannot be understood at the level of individual regulatory proteins, but rather emerges as a result of a complex web of biochemical interactions between multiple proteins, mRNA, and DMA. Our long-term objective to develop computational and experimental methods to dissect and analyze regulatory networks. With respect to human health, one of the most important prokaryotic regulatory networks underlies the type III secretion system (TTSS). The TTSS acts like a molecular syringe to inject bacterial effector proteins into the host cytosol. The TTSS is critical for virulence for many gram-negative pathogens, including Salmonella, Pseudomonas, E. coli, Shigella, Yersinia, Chlamydia, and Bordetella. Of these, the Salmonella TTSS responsible for invading mammalian cells (SPI-1) has the most well-characterized structure and regulation and is the focus of this proposal. In preliminary experiments, we have observed: 1. there is a temporal order in the expression of SPI-1 genes, 2. there is independent control of structural and effector genes, 3. there is hysteresis in the expression of effectors, and 4. the stochastic component of gene expression is differentially controlled. Based on these experiments, we hypothesize that the dynamics are dictated by two genetic circuits in the pathway. The first acts like a multi-signal integrator that commits to SPI-1 expression. The second is a bistable switch, where effectors are irreversibly activated after the needle structure is completed. This proposal seeks to use a combination of experiments, theory, and engineering to quantitatively characterize these circuits. Aim 1: Characterize two genetic circuits in the SPI-1 regulatory pathway. The first is responsible for integrating many environmental inputs and committing to the expression of the TTSS. The second forms a bistable switch that causes effector expression to persist after the input stimulus is removed. Aim 2: Engineer the network dynamics by adding artificial feedback loops. To determine how the topology of regulatory interactions encodes network dynamics, artificial feedback loops will be used to genetically perturb the network. This will provide insight into how complex dynamics evolve.